{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier  # 随机森林\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 载入数据\n",
    "data = np.genfromtxt(\"LR-testSet2.txt\", delimiter=\",\") # 数据集\n",
    "x_data = data[:,:-1]\n",
    "y_data = data[:,-1]\n",
    "\n",
    "plt.scatter(x_data[:,0],x_data[:,1],c=y_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x_data, y_data, test_size = 0.5)\n",
    "# 切分训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(model):\n",
    "    # 获取数据值所在的范围\n",
    "    x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1\n",
    "    y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1\n",
    "\n",
    "    # 生成网格矩阵\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),\n",
    "                         np.arange(y_min, y_max, 0.02))\n",
    "\n",
    "    z = model.predict(np.c_[xx.ravel(), yy.ravel()])# ravel与flatten类似，多维数据转一维。flatten不会改变原始数据，ravel会改变原始数据\n",
    "    z = z.reshape(xx.shape)\n",
    "    # 等高线图\n",
    "    cs = plt.contourf(xx, yy, z)\n",
    "    # 样本散点图\n",
    "    plt.scatter(x_test[:, 0], x_test[:, 1], c=y_test)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7457627118644068"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree = tree.DecisionTreeClassifier()  # 决策树\n",
    "dtree.fit(x_train, y_train)\n",
    "plot(dtree)  \n",
    "dtree.score(x_test, y_test)  #准确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7966101694915254"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 随机森林，n_estimators意思是决策树50棵，bagging50次\n",
    "RF = RandomForestClassifier(n_estimators=50)  \n",
    "RF.fit(x_train, y_train)\n",
    "plot(RF)\n",
    "RF.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 一般随机森林效果要好过决策树"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
